Get Complete eBook Download By email at student.support@hotmail.com Get Complete eBook Download By email at student.support@hotmail.com Get Complete eBook Download By email at student.support@hotmail.com Business Analytics Descriptive Predictive Prescriptive Jeffrey D. Camm James J. Cochran Wake Forest University University of Alabama Michael J. Fry Jeffrey W. Ohlmann University of Cincinnati University of Iowa Australia Brazil Canada Mexico Singapore United Kingdom United States Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may be suppressed. Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. The publisher reserves the right to remove content from this title at any time if subsequent rights restrictions require it. For valuable information on pricing, previous editions, changes to current editions, and alternate formats, please visit www.cengage.com/highered to search by ISBN#, author, title, or keyword for materials in your areas of interest. Important Notice: Media content referenced within the product description or the product text may not be available in the eBook version. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Business Analytics, Fifth Edition Copyright © 2024 Cengage Learning, Inc. ALL RIGHTS RESERVED. Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann No part of this work covered by the copyright herein may be reproduced VP, Product Management & Marketing, Learning Experiences: Thais Alencarr Product Director: Joe Sabatino Senior Portfolio Product Manager: WCN: 02-300 or distributed in any form or by any means, except as permitted by U.S. copyright law, without the prior written permission of the copyright owner. Unless otherwise noted, all content is Copyright © Cengage Learning, Inc. Previous Editions: ©2021, ©2019, ©2017 Aaron Arnsparger Content Manager: Breanna Holmes For product information and technology assistance, contact us at Cengage Customer & Sales Support, 1-800-354-9706 Product Assistant: Flannery Cowan or support.cengage.com. Marketing Manager: Colin Kramer For permission to use material from this text or product, submit all Senior Learning Designer: Brandon Foltz requests online at www.copyright.com. Digital Product Manager: Andrew Southwell Intellectual Property Analyst: Ashley Maynard Intellectual Property Project Manager: Kelli Besse Production Service: MPS Limited Project Manager, MPS Limited: Shreya Tiwari Art Director: Chris Doughman Text Designer: Beckmeyer Design Cover Designer: Beckmeyer Design Cover Image: iStockPhoto.com/tawanlubfah Library of Congress Control Number: 2023900651 Student Edition: ISBN: 978-0-357-90220-2 Loose-leaf Edition ISBN: 978-0-357-90221-9 Cengage 200 Pier 4 Boulevard Boston, MA 02210 USA Cengage is a leading provider of customized learning solutions. Our employees reside in nearly 40 different countries and serve digital learners in 165 countries around the world. Find your local representative at: www.cengage.com. To learn more about Cengage platforms and services, register or access your online learning solution, or purchase materials for your course, visit www.cengage.com. Printed in the United States of America Print Number: 01 Print Year: 2023 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Brief Contents About the Authors xix Preface xxi Chapter 1 Introduction to Business Analytics 1 Chapter 2 Descriptive Statistics 21 Chapter 3 Data Visualization 83 Chapter 4 Data Wrangling: Data Management and Data Cleaning Strategies 151 Chapter 5 Probability: An Introduction to Modeling Uncertainty 199 Chapter 6 Descriptive Data Mining 257 Chapter 7 Statistical Inference 319 Chapter 8 Linear Regression 393 Chapter 9 Time Series Analysis and Forecasting 471 Chapter 10 Predictive Data Mining: Regression Tasks 523 Chapter 11 Predictive Data Mining: Classification Tasks 571 Chapter 12 Spreadsheet Models 633 Chapter 13 Monte Carlo Simulation 671 Chapter 14 Linear Optimization Models 743 Chapter 15 Integer Linear Optimization Models 805 Chapter 16 Nonlinear Optimization Models 849 Chapter 17 Decision Analysis 893 Multi-Chapter Case Problems Capital State University Game-Day Magazines 943 Hanover Inc. 945 Appendix A Basics of Excel 947 Appendix B Database Basics with Microsoft Access 959 Appendix C Solutions to Even-Numbered Problems (Cengage eBook) Appendix D Microsoft Excel Online and Tools for Statistical Analysis (Cengage eBook) References Index Available in the Cengage eBook 997 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Contents About the Authors xix Preface xxi Chapter 1 Introduction to Business Analytics 1 1.1 Decision Making 3 1.2 Business Analytics Defined 3 1.3 A Categorization of Analytical Methods and Models 4 Descriptive Analytics 4 Predictive Analytics 5 Prescriptive Analytics 5 1.4 Big Data, the Cloud, and Artificial Intelligence 6 Volume 6 Velocity 6 Variety 7 Veracity 7 1.5 Business Analytics in Practice 9 Accounting Analytics 9 Financial Analytics 10 Human Resource (HR) Analytics 10 Marketing Analytics 10 Health Care Analytics 11 Supply Chain Analytics 11 Analytics for Government and Nonprofits 11 Sports Analytics 12 Web Analytics 12 1.6 Legal and Ethical Issues in the Use of Data and Analytics 12 Summary 15 Glossary 15 Problems 16 Available in the Cengage eBook: Appendix: Getting Started with R and Rstudio Appendix: Basic Data Manipulation with R Descriptive Statistics 21 Chapter 2 2.1 Overview of Using Data: Definitions and Goals 23 2.2 Types of Data 24 Population and Sample Data 24 Quantitative and Categorical Data 24 Cross-Sectional and Time Series Data 24 Sources of Data 25 2.3 Exploring Data in Excel 27 Sorting and Filtering Data in Excel 27 Conditional Formatting of Data in Excel 30 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com vi Contents 2.4 Creating Distributions from Data 32 Frequency Distributions for Categorical Data 32 Relative Frequency and Percent Frequency Distributions 33 Frequency Distributions for Quantitative Data 35 Histograms 37 Frequency Polygons 42 Cumulative Distributions 43 2.5 Measures of Location 46 Mean (Arithmetic Mean) 46 Median 47 Mode 48 Geometric Mean 48 2.6 Measures of Variability 51 Range 51 Variance 52 Standard Deviation 53 Coefficient of Variation 54 2.7 Analyzing Distributions 54 Percentiles 55 Quartiles 56 z-Scores 56 Empirical Rule 57 Identifying Outliers 59 Boxplots 59 2.8 Measures of Association Between Two Variables 62 Scatter Charts 62 Covariance 64 Correlation Coefficient 67 Summary 68 Glossary 69 Problems 70 Case Problem 1: Heavenly Chocolates Web Site Transactions 80 Case Problem 2: African Elephant Populations 81 Available in the Cengage eBook: Appendix: Descriptive Statistics with R Data Visualization 83 Chapter 3 3.1 Overview of Data Visualization 86 Preattentive Attributes 86 Data-Ink Ratio 89 3.2 Tables 92 Table Design Principles 93 Crosstabulation 94 PivotTables in Excel 97 3.3 Charts 101 Scatter Charts 101 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Contents Recommended Charts in Excel 103 Line Charts 104 Bar Charts and Column Charts 108 A Note on Pie Charts and Three-Dimensional Charts 112 Additional Visualizations for Multiple Variables: Bubble Chart, Scatter Chart Matrix, and Table Lens 112 PivotCharts in Excel 117 3.4 Specialized Data Visualizations 120 Heat Maps 120 Treemaps 121 Waterfall Charts 122 Stock Charts 124 Parallel-Coordinates Chart 126 3.5 Visualizing Geospatial Data 126 Choropleth Maps 127 Cartograms 129 3.6 Data Dashboards 131 Principles of Effective Data Dashboards 131 Applications of Data Dashboards 132 Summary 134 Glossary 134 Problems 136 Case Problem 1: Pelican Stores 149 Case Problem 2: Movie Theater Releases 150 Available in the Cengage eBook: Appendix: Creating Tabular and Graphical Presentations with R Appendix: Data Visualization with Tableau Data Wrangling: Data Management and Data Cleaning Strategies 151 4.1 Discovery 153 Accessing Data 153 The Format of the Raw Data 157 4.2 Structuring 158 Data Formatting 159 Arrangement of Data 159 Splitting a Single Field into Multiple Fields 161 Combining Multiple Fields into a Single Field 165 4.3 Cleaning 167 Missing Data 167 Identification of Erroneous Outliers, Other Erroneous Values, and Duplicate Records 170 4.4 Enriching 176 Subsetting Data 177 Supplementing Data 179 Enhancing Data 182 Chapter 4 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. vii Get Complete eBook Download By email at student.support@hotmail.com viii Contents 4.5 Validating and Publishing 186 Validating 186 Publishing 188 Summary 188 Glossary 189 Problems 190 Case Problem 1: Usman Solutions 197 Available in the Cengage eBook: Appendix: Importing Delimited Files into R Appendix: Working with Records in R Appendix: Working with Fields in R Appendix: Unstacking and Stacking Data in R Probability: An Introduction to Modeling Uncertainty 199 5.1 Events and Probabilities 201 5.2 Some Basic Relationships of Probability 202 Complement of an Event 202 Addition Law 203 5.3 Conditional Probability 205 Independent Events 210 Multiplication Law 210 Bayes’ Theorem 211 5.4 Random Variables 213 Discrete Random Variables 213 Continuous Random Variables 214 5.5 Discrete Probability Distributions 215 Custom Discrete Probability Distribution 215 Expected Value and Variance 217 Discrete Uniform Probability Distribution 220 Binomial Probability Distribution 221 Poisson Probability Distribution 224 5.6 Continuous Probability Distributions 227 Uniform Probability Distribution 227 Triangular Probability Distribution 229 Normal Probability Distribution 231 Exponential Probability Distribution 236 Summary 240 Glossary 240 Problems 242 Case Problem 1: Hamilton County Judges 254 Case Problem 2: McNeil’s Auto Mall 255 Case Problem 3: Gebhardt Electronics 256 Chapter 5 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Contents Available in the Cengage eBook: Appendix: Discrete Probability Distributions with R Appendix: Continuous Probability Distributions with R Descriptive Data Mining 257 Chapter 6 6.1 Dimension Reduction 259 Geometric Interpretation of Principal Component Analysis 259 Summarizing Protein Consumption for Maillard Riposte 262 6.2 Cluster Analysis 266 Measuring Distance Between Observations Consisting of Quantitative Variables 267 Measuring Distance Between Observations Consisting of Categorical Variables 269 k-Means Clustering 271 Hierarchical Clustering and Measuring Dissimilarity Between Clusters 275 Hierarchical Clustering versus k-Means Clustering 283 6.3 Association Rules 284 Evaluating Association Rules 286 6.4 Text Mining 287 Voice of the Customer at Triad Airlines 288 Preprocessing Text Data for Analysis 289 Movie Reviews 290 Computing Dissimilarity Between Documents 293 Word Clouds 294 Summary 295 Glossary 296 Problems 298 Case Problem 1: Big Ten Expansion 315 Case Problem 2: Know Thy Customer 316 Available in the Cengage eBook: Appendix: Principal Component Analysis with R Appendix: k-Means Clustering with R Appendix: Hierarchical Clustering with R Appendix: Association Rules with R Appendix: Text Mining with R Appendix: Principal Component Analysis with Orange Appendix: k-Means Clustering with Orange Appendix: Hierarchical Clustering with Orange Appendix: Association Rules with Orange Appendix: Text Mining with Orange Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. ix Get Complete eBook Download By email at student.support@hotmail.com x Contents Chapter 7 Statistical Inference 319 7.1 Selecting a Sample 322 Sampling from a Finite Population 322 Sampling from an Infinite Population 323 7.2 Point Estimation 326 Practical Advice 328 7.3 Sampling Distributions 328 Sampling Distribution of − x 331 Sampling Distribution of − p 336 7.4 Interval Estimation 339 Interval Estimation of the Population Mean 339 Interval Estimation of the Population Proportion 346 7.5 Hypothesis Tests 349 Developing Null and Alternative Hypotheses 349 Type I and Type II Errors 352 Hypothesis Test of the Population Mean 353 Hypothesis Test of the Population Proportion 364 7.6 Big Data, Statistical Inference, and Practical Significance 367 Sampling Error 367 Nonsampling Error 368 Big Data 369 Understanding What Big Data Is 370 Big Data and Sampling Error 371 Big Data and the Precision of Confidence Intervals 372 Implications of Big Data for Confidence Intervals 373 Big Data, Hypothesis Testing, and p Values 374 Implications of Big Data in Hypothesis Testing 376 Summary 376 Glossary 377 Problems 380 Case Problem 1: Young Professional Magazine 390 Case Problem 2: Quality Associates, Inc. 391 Available in the Cengage eBook: Appendix: Random Sampling with R Appendix: Interval Estimation with R Appendix: Hypothesis Testing with R Linear Regression 393 Chapter 8 8.1 Simple Linear Regression Model 395 Estimated Simple Linear Regression Equation 395 8.2 Least Squares Method 397 Least Squares Estimates of the Simple Linear Regression Parameters 399 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Contents 8.3 Using Excel’s Chart Tools to Compute the Estimated Simple Linear Regression Equation 401 Assessing the Fit of the Simple Linear Regression Model 403 The Sums of Squares 403 The Coefficient of Determination 405 Using Excel’s Chart Tools to Compute the Coefficient of Determination 406 8.4 The Multiple Linear Regression Model 407 Estimated Multiple Linear Regression Equation 407 Least Squares Method and Multiple Linear Regression 408 Butler Trucking Company and Multiple Linear Regression 408 Using Excel’s Regression Tool to Develop the Estimated Multiple Linear Regression Equation 409 8.5 Inference and Linear Regression 412 Conditions Necessary for Valid Inference in the Least Squares Linear Regression Model 413 Testing Individual Linear Regression Parameters 417 Addressing Nonsignificant Independent Variables 420 Multicollinearity 421 8.6 Categorical Independent Variables 424 Butler Trucking Company and Rush Hour 424 Interpreting the Parameters 426 More Complex Categorical Variables 427 8.7 Modeling Nonlinear Relationships 429 Quadratic Regression Models 430 Piecewise Linear Regression Models 434 Interaction Between Independent Variables 436 8.8 Model Fitting 441 Variable Selection Procedures 441 Overfitting 442 8.9 Big Data and Linear Regression 443 Inference and Very Large Samples 443 Model Selection 446 8.10 Prediction with Linear Regression 447 Summary 450 Glossary 450 Problems 452 Case Problem 1: Alumni Giving 466 Case Problem 2: Consumer Research, Inc. 468 Case Problem 3: Predicting Winnings for NASCAR Drivers 469 Available in the Cengage eBook: Appendix: Simple Linear Regression with R Appendix: Multiple Linear Regression with R Appendix: Linear Regression Variable Selection Procedures with R Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. xi Get Complete eBook Download By email at student.support@hotmail.com xii Contents Chapter 9 Time Series Analysis and Forecasting 471 9.1 Time Series Patterns 474 Horizontal Pattern 474 Trend Pattern 476 Seasonal Pattern 477 Trend and Seasonal Pattern 478 Cyclical Pattern 481 Identifying Time Series Patterns 481 9.2 Forecast Accuracy 481 9.3 Moving Averages and Exponential Smoothing 485 Moving Averages 486 Exponential Smoothing 490 9.4 Using Linear Regression Analysis for Forecasting 494 Linear Trend Projection 494 Seasonality Without Trend 496 Seasonality with Trend 497 Using Linear Regression Analysis as a Causal Forecasting Method 500 Combining Causal Variables with Trend and Seasonality Effects 503 Considerations in Using Linear Regression in Forecasting 504 9.5 Determining the Best Forecasting Model to Use 504 Summary 505 Glossary 505 Problems 506 Case Problem 1: Forecasting Food and Beverage Sales 515 Case Problem 2: Forecasting Lost Sales 515 Appendix 9.1: Using the Excel Forecast Sheet 517 Available in the Cengage eBook: Appendix: Forecasting with R Predictive Data Mining: Regression Tasks 523 Chapter 10 10.1 Regression Performance Measures 524 10.2 Data Sampling, Preparation, and Partitioning 526 Static Holdout Method 526 k-Fold Cross-Validation 530 10.3 k-Nearest Neighbors Regression 535 10.4 Regression Trees 538 Constructing a Regression Tree 538 Generating Predictions with a Regression Tree 541 Ensemble Methods 543 10.5 Neural Network Regression 548 Structure of a Neural Network 548 How a Neural Network Learns 552 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Contents 10.6 Feature Selection 555 Wrapper Methods 556 Filter Methods 556 Embedded Methods 557 Summary 558 Glossary 558 Problems 560 Case Problem: Housing Bubble 568 Available in the Cengage eBook: Appendix: k-Nearest Neighbors Regression with R Appendix: Individual Regression Trees with R Appendix: Random Forests of Regression Trees with R Appendix: Neural Network Regression with R Appendix: Regularized Linear Regression with R Appendix: k-Nearest Neighbors Regression with Orange Appendix: Individual Regression Trees with Orange Appendix: Random Forests of Regression Trees with Orange Appendix: Neural Network Regression with Orange Appendix: Regularized Linear Regression with Orange Predictive Data Mining: Classification Tasks 571 Chapter 11 11.1 Data Sampling, Preparation, and Partitioning 573 Static Holdout Method 573 k-Fold Cross-Validation 574 Class Imbalanced Data 574 11.2 Performance Measures for Binary Classification 576 11.3 Classification with Logistic Regression 582 11.4 k-Nearest Neighbors Classification 587 11.5 Classification Trees 591 Constructing a Classification Tree 591 Generating Predictions with a Classification Tree 593 Ensemble Methods 594 11.6 Neural Network Classification 600 Structure of a Neural Network 601 How a Neural Network Learns 605 11.7 Feature Selection 609 Wrapper Methods 609 Filter Methods 610 Embedded Methods 610 Summary 612 Glossary 612 Problems 615 Case Problem: Grey Code Corporation 630 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. xiii Get Complete eBook Download By email at student.support@hotmail.com xiv Contents Available in the Cengage eBook: Appendix: Classification via Logistic Regression with R Appendix: k-Nearest Neighbors Classification with R Appendix: Individual Classification Trees with R Appendix: Random Forests of Classification Trees with R Appendix: Neural Network Classification with R Appendix: Classification via Logistic Regression with Orange Appendix: k-Nearest Neighbors Classification with Orange Appendix: Individual Classification Trees with Orange Appendix: Random Forests of Classification Trees with Orange Appendix: Neural Network Classification with Orange Spreadsheet Models 633 Chapter 12 12.1 Building Good Spreadsheet Models 635 Influence Diagrams 635 Building a Mathematical Model 635 Spreadsheet Design and Implementing the Model in a Spreadsheet 637 12.2 What-If Analysis 640 Data Tables 640 Goal Seek 642 Scenario Manager 644 12.3 Some Useful Excel Functions for Modeling 649 SUM and SUMPRODUCT 650 IF and COUNTIF 651 XLOOKUP 654 12.4 Auditing Spreadsheet Models 656 Trace Precedents and Dependents 656 Show Formulas 656 Evaluate Formulas 658 Error Checking 658 Watch Window 659 12.5 Predictive and Prescriptive Spreadsheet Models 660 Summary 661 Glossary 661 Problems 662 Case Problem: Retirement Plan 670 Monte Carlo Simulation 671 Chapter 13 13.1 Risk Analysis for Sanotronics LLC 673 Base-Case Scenario 673 Worst-Case Scenario 674 Best-Case Scenario 674 Sanotronics Spreadsheet Model 674 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Contents xv Use of Probability Distributions to Represent Random Variables 676 Generating Values for Random Variables with Excel 677 Executing Simulation Trials with Excel 681 Measuring and Analyzing Simulation Output 682 13.2 Inventory Policy Analysis for Promus Corp 686 Spreadsheet Model for Promus 687 Generating Values for Promus Corp’s Demand 688 Executing Simulation Trials and Analyzing Output 691 13.3 Simulation Modeling for Land Shark Inc. 693 Spreadsheet Model for Land Shark 694 Generating Values for Land Shark’s Random Variables 696 Executing Simulation Trials and Analyzing Output 698 Generating Bid Amounts with Fitted Distributions 700 13.4 Simulation with Dependent Random Variables 709 Spreadsheet Model for Press Teag Worldwide 709 13.5 Simulation Considerations 714 Verification and Validation 714 Advantages and Disadvantages of Using Simulation 714 Summary 715 Summary of Steps for Conducting a Simulation Analysis 715 Glossary 716 Problems 717 Case Problem 1: Four Corners 731 Case Problem 2: Ginsberg’s Jewelry Snowfall Promotion 732 Appendix 13.1: Common Probability Distributions for Simulation 734 Linear Optimization Models 743 Chapter 14 14.1 A Simple Maximization Problem 745 Problem Formulation 746 Mathematical Model for the Par, Inc. Problem 748 14.2 Solving the Par, Inc. Problem 749 The Geometry of the Par, Inc. Problem 749 Solving Linear Programs with Excel Solver 751 14.3 A Simple Minimization Problem 755 Problem Formulation 755 Solution for the M&D Chemicals Problem 755 14.4 Special Cases of Linear Program Outcomes 757 Alternative Optimal Solutions 758 Infeasibility 759 Unbounded 760 14.5 Sensitivity Analysis 762 Interpreting Excel Solver Sensitivity Report 762 14.6 General Linear Programming Notation and More Examples 764 Investment Portfolio Selection 765 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com xvi Contents Transportation Planning 768 Maximizing Banner Ad Revenue 772 Assigning Project Leaders to Clients 776 Diet Planning 779 14.7 Generating an Alternative Optimal Solution for a Linear Program 782 Summary 783 Glossary 784 Problems 785 Case Problem1: Investment Strategy 801 Case Problem 2: Solutions Plus 802 Available in the Cengage eBook: Appendix: Linear Programming with R Integer Linear Optimization Models 805 Chapter 15 15.1 Types of Integer Linear Optimization Models 806 15.2 Eastborne Realty, an Example of Integer Optimization 807 The Geometry of Linear All-Integer Optimization 808 15.3 Solving Integer Optimization Problems with Excel Solver 810 A Cautionary Note About Sensitivity Analysis 813 15.4 Applications Involving Binary Variables 815 Capital Budgeting 815 Fixed Cost 816 Bank Location 820 Product Design and Market Share Optimization 822 15.5 Modeling Flexibility Provided by Binary Variables 825 Multiple-Choice and Mutually Exclusive Constraints 825 k Out of n Alternatives Constraint 826 Conditional and Corequisite Constraints 826 15.6 Generating Alternatives in Binary Optimization 827 Summary 829 Glossary 830 Problems 830 Case Problem 1: Applecore Children’s Clothing 845 Case Problem 2: Yeager National Bank 847 Available in the Cengage eBook: Appendix: Integer Programming with R Nonlinear Optimization Models 849 Chapter 16 16.1 A Production Application: Par, Inc. Revisited 850 An Unconstrained Problem 850 A Constrained Problem 851 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Contents 16.2 16.3 16.4 16.5 Solving Nonlinear Optimization Models Using Excel Solver 853 Sensitivity Analysis and Shadow Prices in Nonlinear Models 855 Local and Global Optima 856 Overcoming Local Optima with Excel Solver 858 A Location Problem 860 Markowitz Portfolio Model 861 Adoption of a New Product: The Bass Forecasting Model 866 16.6 Heuristic Optimization Using Excel’s Evolutionary Method 869 Summary 877 Glossary 877 Problems 878 Case Problem: Portfolio Optimization with Transaction Costs 889 Available in the Cengage eBook: Appendix: Nonlinear Programming with R Decision Analysis 893 Chapter 17 17.1 Problem Formulation 895 Payoff Tables 896 Decision Trees 896 17.2 Decision Analysis Without Probabilities 897 Optimistic Approach 897 Conservative Approach 898 Minimax Regret Approach 898 17.3 Decision Analysis with Probabilities 900 Expected Value Approach 900 Risk Analysis 902 Sensitivity Analysis 903 17.4 Decision Analysis with Sample Information 904 Expected Value of Sample Information 909 Expected Value of Perfect Information 909 17.5 Computing Branch Probabilities with Bayes’ Theorem 910 17.6 Utility Theory 913 Utility and Decision Analysis 914 Utility Functions 918 Exponential Utility Function 921 Summary 923 Glossary 923 Problems 925 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. xvii Get Complete eBook Download By email at student.support@hotmail.com xviii Contents Case Problem 1: Property Purchase Strategy 939 Case Problem 2: Semiconductor Fabrication at Axeon Labs 941 Multi-Chapter Case Problems Capital State University Game-Day Magazines 943 Hanover Inc. 945 Appendix A Basics of Excel 947 Appendix B Database Basics with Microsoft Access 959 Appendix C Solutions to Even-Numbered Problems (Cengage eBook) Appendix D Microsoft Excel Online and Tools for Statistical Analysis (Cengage eBook) References Index Available in the Cengage eBook 997 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com About the Authors Jeffrey D. Camm. Jeffrey D. Camm is the Inmar Presidential Chair in Analytics and Senior Associate Dean of Faculty in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he was on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published over 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, Interfaces, and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the recipient of the 2006 INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010, he served as editor-in-chief of INFORMS Journal on Applied Analytics (formerly Interfaces). In 2017, he was named an INFORMS Fellow. James J. Cochran. James J. Cochran is Professor of Applied Statistics, the Rogers-Spivey Faculty Fellow, and Associate Dean for Faculty and Research at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. degrees from Wright State University and his Ph.D. from the University of Cincinnati. He has been at the University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa, and Pole Universitaire Leonard de Vinci. Professor Cochran has published over 50 papers in the development and application of operations research and statistical methods. He has published his research in Management Science, The American Statistician, Communications in Statistics—Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal of Applied Analytics, Statistics and Probability Letters, and other professional journals. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. Professor Cochran was elected to the International Statistics Institute in 2005 and named a Fellow of the American Statistical Association in 2011. He received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In 2017 he received the American Statistical Association's Waller Distinguished Teaching Career Award and was named a Fellow of INFORMS, and in 2018 he received the INFORMS President's Award. He has twice been recognized as a finalist for the Innovative Applications in Analytics Award. A strong advocate for effective statistics and operations research education as a means of improving the quality of applications to real problems, Professor Cochran has organized and chaired teaching effectiveness workshops in Montevideo, Uruguay; Cape Town, South Africa; Cartagena, Colombia; Jaipur, India; Buenos Aires, Argentina; Nairobi, Kenya; Buea, Cameroon; Kathmandu, Nepal; Osijek, Croatia; Havana, Cuba; Ulaanbaatar, Mongolia; Chişinău, Moldova; Dar es Salaam, Tanzania; Sozopol, Bulgaria; Tunis, Tunisia; and Saint George's, Grenada. He has served as an operations research consultant to numerous companies and not-for-profit organizations. He served as editor-in-chief of INFORMS Transactions on Education from 2006 to 2012 and is on the editorial board of INFORMS Journal on Applied Analytics (formerly Interfaces), International Transactions in Operational Research, and Significance. Michael J. Fry. Michael J. Fry is Professor of Operations, Business Analytics, and Information Systems and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University and M.S.E. and Ph.D. degrees from the Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com xx About the Authors University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously Department Head and has been named a Lindner Research Fellow. He has also been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at Cornell University and the Sauder School of Business at the University of British Columbia. Professor Fry has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IISE Transactions, Critical Care Medicine, and INFORMS Journal on Applied Analytics (formerly Interfaces). His research has been funded by the National Science Foundation and other funding agencies. His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics, and public-policy operations. He has worked with many different organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals, and the Cincinnati Zoo & Botanical Garden. He was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati. Jeffrey W. Ohlmann. Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska, and M.S. and Ph.D. degrees from the University of Michigan. He has been at the University of Iowa since 2003. Professor Ohlmann's research on the modeling and solution of decision-making problems has produced more than two dozen research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, the European Journal of Operational Research, and INFORMS Journal on Applied Analytics (formerly Interfaces). He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections, and three National Football League franchises. Because of the relevance of his work to industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Preface B usiness Analytics 5E is designed to introduce the concept of business analytics to undergraduate and graduate students. This edition builds upon what was one of the first collections of materials that are essential to the growing field of business analytics. In Chapter 1, we present an overview of business analytics and our approach to the material in this textbook. In simple terms, business analytics helps business professionals make better decisions based on data. We discuss exploring, wrangling, summarizing, visualizing, and understanding data in Chapters 2 through 5. In Chapter 6, we introduce additional models to describe data and the relationships among variables. Chapters 7 through 11 introduce methods for both gaining insights from historical data and predicting possible future outcomes. Chapter 12 covers the use of spreadsheets for examining data and building decision models. In Chapter 13, we demonstrate how to explicitly introduce uncertainty into spreadsheet models through the use of Monte Carlo simulation. In Chapters 14 through 16, we discuss optimization models to help decision makers choose the best decision based on the available data. Chapter 17 is an overview of decision analysis approaches for incorporating a decision maker’s views about risk into decision making. In Appendix A we present optional material for students who need to learn the basics of using Microsoft Excel. The use of databases and manipulating data in Microsoft Access is discussed in Appendix B. Appendixes in many chapters illustrate the use of additional software tools such as Tableau, R, and Orange to apply analytics methods. This textbook can be used by students who have previously taken a course on basic statistical methods as well as students who have not had a prior course in statistics. Business Analytics 5E is also amenable to a two-course sequence in business statistics and analytics. All statistical concepts contained in this textbook are presented from a business analytics perspective using practical business examples. Chapters 2, 5, 7, 8, and 9 provide an introduction to basic statistical concepts that form the foundation for more advanced analytics methods. Chapter 2 introduces descriptive statistical measures such as measures of location, measures of variability, methods to analyze distributions of data, and measures of association between variables. Chapter 5 covers material related to probability including discussions of discrete probability distributions and continuous probability distributions. Chapter 7 contains topics related to statistical inference including interval estimation and hypothesis testing. Chapter 8 explores linear regression models including simple linear models and multiple linear regression models, and chapter 9 covers methods for forecasting and time series data. Chapter 3 covers additional data visualization topics and Chapter 4 is an overview of approaches for exploring, cleaning, and wrangling data to make the data more amenable for analysis. These topics are not always covered in traditional business statistics courses, but they are essential in understanding how to analyze data. Chapters 6, 10, and 11 cover additional topics in data mining that are not traditionally part of most introductory business statistics courses, but they are exceedingly important and commonly used in current business environments. Chapter 6 focuses on data mining methods used to describe data and the relationships among variables such as cluster analysis, association rules and text mining. Chapter 6 also contains an overview of dimension reduction techniques such as principal component analysis. Chapters 10 and 11 focus on data mining methods used to make predictions from data. Chapter 10 covers models for regression tasks such as k-nearest neighbors, regression trees, and neural networks. Chapter 11 covers models for classification tasks such as logistic regression, k-nearest neighbors, classification trees, ensemble methods, and neural networks. Both chapters 10 and 11 cover data sampling methods for prediction models including k-fold cross-validation, and also provide an overview of feature selection methods such as wrapper methods, filter methods, and embedded methods (including regularization). Chapter 12 and Appendix A provide the foundational knowledge students need to use Microsoft Excel for analytics applications. Chapters 13 through 17 build upon this spreadsheet knowledge to present additional topics that are used by many organizations that use prescriptive analytics to improve decision making. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com xxii Preface Updates in the Fifth Edition The fifth edition of Business Analytics is a major revision that introduces new chapters, new concepts, and new tools. Chapter 4 is a new chapter on data wrangling that covers topics such as how to access and structure data for exploration, how to clean and enrich data to facilitate analysis, and how to validate data. Our coverage of data mining topics has been greatly expanded. Our coverage of descriptive data mining techniques in Chapter 6 now includes a discussion of how to conduct dimension reduction with principal component analysis (PCA), and we have thoroughly updated our coverage of clustering, association rules, and text mining. Our coverage of predictive data mining techniques now includes two separate chapters: Chapter 10 focuses on predicting quantitative outcomes with k-nearest neighbors regression, regression trees, ensemble methods, and neural network regression, while Chapter 11 focuses on predicting binary categorical outcomes with k-nearest neighbors classification, classification trees, ensemble methods, and neural network classification. In addition, we now include online appendixes that introduce the software package Orange for descriptive and predictive data-mining models. Orange is an open-source machine learning and data visualization software package built using Python. This coverage of Orange and Python complements our existing coverage of R for descriptive and predictive analytics. Our coverage of descriptive analytics methods in Chapter 2 and data visualization in Chapter 3 has been greatly expanded to provide additional depth and introduce new methods related to these topics. We have also increased the size of many data sets in Chapter 8 on linear regression and Chapter 9 on time series analysis and forecasting to better represent realistic data sets that are encountered in practice. We have added additional topics to our coverage of optimization models including a new section in Chapter 16 on heuristic optimization with the Excel Solver Evolutionary method. We also now provide a new online Appendix D that covers the basics of the use of Microsoft Excel Online for statistical analysis. Finally, we have added learning objectives (LOs) to the beginning of each chapter that explain the key concepts covered in each chapter, and we map these LOs to the end-of-chapter problems and cases. ● ● ● ● New Chapter on Data Wrangling. Chapter 4 covers methods for accessing and structuring data for exploring, cleaning and enriching data to facilitate analysis, and validating data. Many professionals in analytics and data science spend much of their time preparing data for exploration and analysis. It is essential that students are familiar with these methods. We have also created new online appendixes covering how to implement these methods in R. New Material in Descriptive Data Mining Chapter. Chapter 6 on descriptive data mining techniques now provides a more in-depth discussion of dimension reduction techniques such as principal component analysis. We present material to help students understand what this approach is doing, as well as how to interpret the output from this method. We have also rewritten many sections in this chapter to provide additional explanation and added context to the data-mining methods introduced. New Material in Predictive Data Mining Chapters. We have divided the previous single chapter on predictive data mining methods into two different chapters: Chapter 10 covers regression data mining models, and Chapter 11 covers classification data mining models. Splitting this chapter into two chapters allows us to include additional methods and more thoroughly explain the methods and output. Chapter 10 focuses on predicting quantitative outcomes with k-nearest neighbors regression, regression trees, and neural network regression. Chapter 11 focuses on predicting binary categorical outcomes with logistic regression, k-nearest neighbors classification, classification trees, and neural network classification. New Online Appendixes for Using Python-Based Orange for Data Mining. We now include online appendixes that introduce the software package Orange for descriptive and predictive data-mining models. Orange is an open-source machine learning and data visualization software package built using Python. It provides an easy-to-use, yet powerful, workflow-based approach to building analytics models. The use of Python by Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Preface xxiii analytics professionals has increased greatly, and Orange provides an excellent introduction to using Python in an easy-to-learn environment. These appendixes include practice problems for students to solve using Orange. ● ● ● ● ● ● ● Updates to R Appendixes for Data Mining Chapters. We continue to include online appendixes for using the popular open-source software package R for descriptive and predictive data mining. We have revised these online appendixes to focus on using script R commands in RStudio rather than the Rattle interface. We have found the use of script R through RStudio to be more robust and more stable than the use of Rattle. To facilitate the teaching of this material, we include complete R script files for all examples used throughout the R appendixes included for this this textbook as well as detailed step-by-step instructions. Practice Problems in Online Appendixes for Orange and R. Each online appendix for using Orange or R now includes practice problems that can be solved using these popular open-source software packages. The practice problems include specific hints for how to solve these problems using each software. Problem solutions, including the necessary code files, using these software packages are available to instructors for all practice problems. Additional Coverage of Descriptive Analytics and Data Visualization. We have added additional coverage in Chapters 2 and 3 to provide more depth of our coverage of descriptive analytics methods and data visualization. In Chapter 2, we have rewritten the explanation of histograms and we have added a discussion of frequency polygons to supplement the coverage of histograms as a way of exploring distributions of data. Chapter 3’s coverage of data visualization now includes a more comprehensive discussion of best practices in data visualization including an explanation of the use of preattentive attributes and the data-ink ratio to create effective tables and charts. We have also rearranged the chapter and added coverage of table lens, waterfall charts, stock charts, choropleth maps, and cartograms for data visualization. New Material for Optimization. We have expanded our coverage of optimization models. Chapter 14 on Linear Optimization Models now includes additional models for transportation problems, diet problems, and assignment problems. Chapter 16 on nonlinear optimization models contains a new section on heuristic optimization using Excel’s Evolutionary Solver method that can be used to solve complex optimization models. We have also added online appendixes for solving optimization models in R. Increased Size of Data Sets. We have increased the size of many data sets in Chapter 8 on linear regression and Chapter 9 on time series analysis and forecasting. These larger data sets provide better representations of realistic data sets that are encountered in practice, and they are more useful for conducting inference and examining the underlying assumptions made in regression and time series analysis. Learning Objectives. We have added Learning Objectives (LOs) to the beginning of each chapter. These LOs explain the key concepts that are covered in each chapter. The LOs are also mapped onto each end-of-chapter problem and case so instructors can easily identify which LOs are covered by each problem. New End-of-Chapter Problems and Cases. The fifth edition of this textbook includes over 175 new problems and 6 new cases. We have added problems to Chapter 1 so that now each chapter in the textbook includes end-of-chapter problems. We have divided the end-of-chapter problems in Chapters 6, 10, and 11 covering data mining topics into conceptual problems and software application problems. Conceptual problems do not require dedicated software to answer, and we have added many new conceptual problems to each data mining chapter. Software application problems require the use of dedicated software, and allow students to apply the tools covered in these chapters. The software application problems in these chapters are also provided in the online appendixes for the use of Orange and R for data mining applications. The online appendix version of these Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com xxiv Preface problems include specific hints for how to solve the problems in the corresponding software. As we have done in past editions, Excel solution files are available to instructors for many of the problems and cases that require the use of Excel. For software application problems that require the use of software in the data-mining chapters (Chapters 6, 10, and 11), we include solutions for both Orange and R. Continued Features and Pedagogy In the fifth edition of this textbook, we continue to offer all of the features that have been successful in the previous editions. Some of the specific features that we use in this textbook are listed below. ● ● ● ● ● Integration of Microsoft Excel: Excel has been thoroughly integrated throughout this textbook. For many methodologies, we provide instructions for how to perform calculations both by hand and with Excel. In other cases where realistic models are practical only with the use of a spreadsheet, we focus on the use of Excel to describe the methods to be used. Excel instructions have been fully updated throughout the textbook to match the latest versions of Excel most likely to be used by students. The textbook assumes the use of desktop versions of Excel for most problems and examples, but the accompanying online Appendix D covers the basics of the use of Microsoft Excel Online. Notes and Comments: At the end of many sections, we provide Notes and Comments to give the student additional insights about the methods presented in that section. These insights include comments on the limitations of the presented methods, recommendations for applications, and other matters. Additionally, margin notes are used throughout the textbook to provide additional insights and tips related to the specific material being discussed. Analytics in Action: Each chapter contains an Analytics in Action article. These articles present interesting examples of the use of business analytics in practice. The examples are drawn from many different organizations in a variety of areas including healthcare, finance, manufacturing, marketing, and others. DATAfiles and MODELfiles: All data sets used as examples and in student exercises are also provided online on the companion site as files available for download by the student. DATAfiles are files that contain data needed for the examples and problems given in the textbook. Typically, the DATAfiles are in .xlsx format for Excel or .csv format for import into other software packages. MODELfiles contain additional modeling features such as extensive use of Excel formulas or the use of Excel Solver, script files for R, or workflow models for Orange. Problems and Cases: Each chapter, now including Chapter 1, contains an extensive selection of problems to help the student master the material presented in that chapter. The problems vary in difficulty and most relate to specific examples of the use of business analytics in practice. Answers to selected even-numbered problems are provided in an online supplement for student access. With the exception of Chapter 1, each chapter also includes at least one in-depth case study that connects many of the different methods introduced in the chapter. The case studies are designed to be more open-ended than the chapter problems, but enough detail is provided to give the student some direction in solving the cases. We continue to include two cases at the end of the textbook that require the use of material from multiple chapters in the text to better illustrate how concepts from different chapters relate to each other. MindTap MindTap is a customizable digital course solution that includes an interactive eBook, autograded exercises from the textbook, algorithmic practice problems with solutions feedback, Excel Online problems, Exploring Analytics visualizations, Adaptive Test Prep, videos, and Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Preface xxv more! MindTap is also where instructors and users can find the online appendixes for R and Orange. All of these materials offer students better access to resources to understand the materials within the course. For more information on MindTap, please contact your Cengage representative. WebAssign Prepare for class with confidence using WebAssign from Cengage. This online learning platform fuels practice, so students can truly absorb what you learn—and are better prepared come test time. Videos, Problem Walk-Throughs, and End-of-Chapter problems and cases with instant feedback help them understand the important concepts, while instant grading allows you and them to see where they stand in class. WebAssign is also where instructors and users can find the online appendixes for R and Orange. Class Insights allows students to see what topics they have mastered and which they are struggling with, helping them identify where to spend extra time. Study Smarter with WebAssign. Instructor and Student Resources Additional instructor and student resources for this product are available online. Instructor assets include a Solutions and Answers Guide, Instructor’s Manual, PowerPoint® slides, and a test bank powered by Cognero®. Prepared by the authors, solutions for software application problems and cases in chapters 6, 10, and 11 are available using both R and Orange. Student assets include DATAfiles and MODELfiles that accompany the chapter examples and problems as well as solutions to selected even-numbered chapter problems. Sign up or sign in at www.cengage.com to search for and access this product and its online resources. Acknowledgments We would like to acknowledge the work of reviewers and users who have provided comments and suggestions for improvement of this text. Thanks to: Rafael Becerril Arreola University of South Carolina Matthew D. Bailey Bucknell University Phillip Beaver University of Denver M. Khurrum S. Bhutta Ohio University Paolo Catasti Virginia Commonwealth University Q B. Chung Villanova University Elizabeth A. Denny University of Kentucky Mike Taein Eom University of Portland Yvette Njan Essounga Fayetteville State University Lawrence V. Fulton Texas State University Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com xxvi Preface Tom Groleau Carthage College James F. Hoelscher Lincoln Memorial University Eric Huggins Fort Lewis College Faizul Huq Ohio University Marco Lam York College of Pennsylvania Thomas Lee University of California, Berkeley Roger Myerson Northwestern University Ram Pakath University of Kentucky Susan Palocsay James Madison University Andy Shogan University of California, Berkeley Dothan Truong Embry-Riddle Aeronautical University Kai Wang Wake Technical Community College Ed Wasil American University Ed Winkofsky University of Cincinnati A special thanks goes to our associates from business and industry who supplied the Analytics in Action features. We recognize them individually by a credit line in each of the articles. We are also indebted to our senior product manager, Aaron Arnsparger; our Senior Content Manager, Breanna Holmes; senior learning designer, Brandon Foltz; subject matter expert, Deborah Cernauskas; digital project manager, Andrew Southwell; and our project manager at MPS Limited, Shreya Tiwari, for their editorial counsel and support during the preparation of this text. Jeffrey D. Camm James J. Cochran Michael J. Fry Jeffrey W. Ohlmann Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Chapter 1 Introduction to Business Analytics Contents 1.1 Decision Making 1.2 Business Analytics Defined 1.3 A Categorization of Analytical Methods and Models Descriptive Analytics Predictive Analytics Prescriptive Analytics 1.4 Big Data, the Cloud, and Artificial Intelligence Volume Velocity Variety Veracity 1.5 Business Analytics in Practice Accounting Analytics Financial Analytics Human Resource (HR) Analytics Marketing Analytics Health Care Analytics Supply Chain Analytics Analytics for Government and Nonprofits Sports Analytics Web Analytics 1.6 Legal and Ethical Issues in the Use of Data and Analytics Summary 15 Glossary 15 Problems 16 Available in the Cengage eBook: Appendix: Getting Started with R and Rstudio Appendix: Basic Data Manipulation with R Learning Objectives After completing this chapter, you will be able to: LO 1 Identify strategic, tactical, and operational decisions. LO 2 Describe the steps in the decision-making process. LO 3 Identify examples of descriptive, predictive, and prescriptive analytics. LO 4 Describe applications of analytics for decision making. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 2 Chapter 1 Introduction to Business Analytics In many situations, data are often abundant and can be used to guide decision-making. Suppose you apply for a loan for the first time. How does the bank assess the riskiness of the loan it might make to you? How does Amazon.com know which books and other products to recommend to you when you log in to their web site? How do airlines determine what price to quote to you when you are shopping for a plane ticket? You may be applying for a loan for the first time, but millions of people around the world have applied for loans before. Many of these loan recipients have paid back their loans in full and on time, but some have not. The bank wants to know whether you are more like those who have paid back their loans or more like those who defaulted. By comparing your credit history, financial situation, and other factors to the vast database of previous loan recipients, the bank can effectively assess how likely you are to default on a loan. Similarly, Amazon.com has access to data on millions of purchases made by customers on its web site. Amazon.com examines your previous purchases, the products you have viewed, and any product recommendations you have provided. Amazon.com then searches through its huge database for customers who are similar to you in terms of product purchases, recommendations, and interests. Once similar customers have been identified, their purchases form the basis of the recommendations given to you. Prices for airline tickets are frequently updated. The price quoted to you for a flight between New York and San Francisco today could be very different from the price that will be quoted tomorrow. These changes happen because airlines use a pricing strategy known as revenue management. Revenue management works by examining vast amounts of data on past airline customer purchases and using these data to forecast future purchases. These forecasts are then fed into sophisticated optimization algorithms that determine the optimal price to charge for a particular flight and when to change that price. Revenue management has resulted in substantial increases in airline revenues. This book is concerned with data-driven decision making and the use of analytical approaches in the decision-making process. Three developments spurred recent explosive growth in the use of analytical methods in business applications. First, technological advances have enabled the ability to track and store large amounts of data. Improved point-of-sale scanner technology, tracking of activity on the internet (e.g,. e-commerce and social networks), sensors on mechanical devices such as aircraft engines, automobiles, and farm machinery through the Internet-of-Things, and personal electronic devices produce incredible amounts of data for businesses. Naturally, businesses want to use these data to improve the efficiency and profitability of their operations, better understand their customers, price their products more effectively, and gain a competitive advantage. Second, ongoing research has resulted in numerous methodological developments to extract knowledge from the data. Examples of these are advances in computational approaches to effectively handle and explore massive amounts of data, faster algorithms for optimization and simulation, and more effective approaches for visualizing data. Third, these methodological developments are paired with an explosion in computing power. Faster computer chips, parallel computing, and cloud computing (the remote use of hardware and software over the Internet) have enabled businesses to solve big problems more quickly and more accurately than ever before. In summary, the availability of massive amounts of data, improvements in analytic methodologies, and substantial increases in computing power have all come together to result in a dramatic upsurge in the use of analytical methods in business and a reliance on the discipline that is the focus of this text: business analytics. The purpose of this text is to provide students with a sound conceptual understanding of the role that business analytics plays in the decision-making process and to provide a better understanding of the variety of applications in which analytical methods have been used successfully. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 1.1 Decision Making 3 1.1 Decision Making It is the responsibility of managers to plan, coordinate, organize, and lead their organizations to better performance. Ultimately, managers’ responsibilities require that they make strategic, tactical, or operational decisions. Strategic decisions involve higher-level issues concerned with the overall direction of the organization; these decisions define the organization’s overall goals and aspirations for the future. Strategic decisions are usually the domain of higher-level executives and have a time horizon of three to five years. Tactical decisions concern how the organization should achieve the goals and objectives set by its strategy, and they are usually the responsibility of midlevel management. Tactical decisions usually span a year and thus are revisited annually or even every six months. Operational decisions affect how the firm is run from day to day; they are the domain of operations managers, who are the closest to the customer. Consider the case of the Thoroughbred Running Company (TRC). Historically, TRC had been a catalog-based retail seller of running shoes and apparel. TRC sales revenues grew quickly as it changed its emphasis from catalog-based sales to Internet-based sales. Recently, TRC decided that it should also establish retail stores in the malls and downtown areas of major cities. This strategic decision will take the firm in a new direction that it hopes will complement its Internet-based strategy. TRC middle managers will therefore have to make a variety of tactical decisions in support of this strategic decision, including how many new stores to open this year, where to open these new stores, how many distribution centers will be needed to supply the new stores, and where to locate these distribution centers. Operations managers in the stores will need to make day-to-day decisions regarding, for instance, how many pairs of each model and size of shoes to order from the distribution centers and how to schedule their sales personnel’s work time. Regardless of the level within the firm, decision making can be defined as the following process: 1. Identify and define the problem. 2. Determine the criteria that will be used to evaluate alternative solutions. 3. Determine the set of alternative solutions. 4. Evaluate the alternatives. 5. Choose an alternative. Step 1 of decision making, identifying and defining the problem, is the most critical. Only if the problem is well-defined, with clear metrics of success or failure (step 2), can a proper approach for solving the problem (steps 3 and 4) be devised. Decision making concludes with the choice of one of the alternatives (step 5). There are a number of approaches to making decisions: tradition (“We’ve always done it this way”), intuition (“gut feeling”), and rules of thumb (“As the restaurant owner, I schedule twice the number of waiters and cooks on holidays”). The power of each of these approaches should not be underestimated. Managerial experience and intuition are valuable inputs to making decisions, but what if relevant data are available to help us make moreinformed decisions? The vast amounts of data now generated and stored electronically provide tremendous opportunity for businesses to improve their profitability and service to their customers. How can managers convert these data into knowledge they can use to be more efficient and effective in managing their businesses? 1.2 Business Analytics Defined What makes decision making difficult and challenging? Uncertainty is probably the number one challenge. If we knew how much the demand would be for our product, we could do a much better job of planning and scheduling production. If we knew exactly how long each step in a project would take to be completed, we could better predict the project’s cost and completion date. If we knew how stocks would perform, investing would be a lot easier. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 4 Chapter 1 Some firms and industries use the simpler term, analytics. Analytics is often thought of as a broader category than business analytics, encompassing the use of analytical techniques in the sciences and engineering as well. In this text, we use business analytics and analytics synonymously. Another factor that makes decision making difficult is that we often face such an enormous number of alternatives that we cannot evaluate them all. What is the best combination of stocks to help me meet my financial objectives? What is the best product line for a company that wants to maximize its market share? How should an airline price its tickets so as to maximize revenue? Business analytics is the scientific process of transforming data into insight for making better decisions.1 Business analytics is used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making. As we shall see, the tools of business analytics can aid decision making by creating insights from data, by improving our ability to more accurately forecast for planning, by helping us quantify risk, and by yielding better alternatives through analysis and optimization. A study based on a large sample of firms that was conducted by researchers at MIT’s Sloan School of Management and the University of Pennsylvania concluded that firms guided by data-driven decision making have higher productivity and market value and increased output and profitability.2 Introduction to Business Analytics 1.3 A Categorization of Analytical Methods and Models Business analytics can involve anything from simple reports to the most advanced optimization techniques (methods for finding the best course of action). Analytics is generally thought to comprise three broad categories of techniques: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive Analytics Appendix B, at the end of this book, describes how to use Microsoft Access to conduct data queries. Descriptive analytics encompasses the set of techniques that describes what has happened in the past. Examples are data queries, reports, descriptive statistics, data visualization including data dashboards, unsupervised learning techniques from data mining, and basic spreadsheet models. A data query is a request for information with certain characteristics from a database. For example, a query to a manufacturing plant’s database might be for all records of shipments to a particular distribution center during the month of March. This query provides descriptive information about these shipments: the number of shipments, how much was included in each shipment, the date each shipment was sent, and so on. A report summarizing relevant historical information for management might be conveyed by the use of descriptive statistics (means, measures of variation, etc.) and data-visualization tools (tables, charts, and maps). Simple descriptive statistics and data-visualization techniques can be used to find patterns or relationships in a large database. Data dashboards are collections of tables, charts, maps, and summary statistics that are updated as new data become available. Dashboards are used to help management monitor specific aspects of the company’s performance related to their decision-making responsibilities. For corporate-level managers, daily data dashboards might summarize sales by region, current inventory levels, and other company-wide metrics; front-line managers may view dashboards that contain metrics related to staffing levels, local inventory levels, and short-term sales forecasts. Data mining is the use of analytical techniques for better understanding patterns and relationships that exist in large data sets. Data mining includes unsupervised learning techniques which are descriptive methods that seek to identify patterns based on notions of similarity (cluster analysis) or correlation (association rules) in different types of data. For We adopt the definition of analytics developed by the Institute for Operations Research and the Management Sciences (INFORMS). 1 E. Brynjolfsson, L. M. Hitt, and H. H. Kim, “Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?” Thirty-Second International Conference on Information Systems, Shanghai, China, December 2011. 2 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 1.3 A Categorization of Analytical Methods and Models 5 example, by processing text on social network platforms such as Twitter, similar customer comments can be grouped together into clusters to help Apple better understand how its customers are feeling about the Apple Watch. Predictive Analytics Predictive analytics consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another. For example, past data on product sales may be used to construct a mathematical model to predict future sales. This model can factor in the product’s growth trajectory and seasonality based on past patterns. A packaged-food manufacturer may use point-of-sale scanner data from retail outlets to help in estimating the lift in unit sales due to coupons or sales events. Survey data and past purchase behavior may be used to help predict the market share of a new product. All of these are applications of predictive analytics. Traditional statistical methods such as linear regression and time series analysis fall under the banner of predictive analytics. Data mining includes supervised learning techniques which use past data to learn the relationship between an outcome variable of interest and a set of input variables. For example, a large grocery store chain might be interested in developing a targeted marketing campaign that offers a discount coupon on potato chips. By studying historical pointof-sale data, the store may be able to use data mining to predict which customers are the most likely to respond to an offer on discounted potato chips, by purchasing higher-margin items such as beer or soft drinks in addition to the chips, thus increasing the store’s overall revenue. Simulation involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision. For example, banks often use simulation to model investment and default risk in order to stress-test financial models. Simulation is also often used in the pharmaceutical industry to assess the risk of introducing a new drug. Prescriptive Analytics Prescriptive analytics differs from descriptive and predictive analytics in that prescriptive analytics indicates a course of action to take; that is, the output of a prescriptive model is a decision. Predictive models provide a forecast or prediction, but do not provide a decision. However, a forecast or prediction, when combined with a rule, becomes a prescriptive model. For example, we may develop a model to predict the probability that a person will default on a loan. If we create a rule that says if the estimated probability of default is more than 0.6, we should not award a loan, now the predictive model, coupled with the rule is prescriptive analytics. These types of prescriptive models that rely on a rule or set of rules are often referred to as rule-based models. Other examples of prescriptive analytics are portfolio models in finance, supply network design models in operations, and price-markdown models in retailing. Portfolio models use historical investment return data to determine which mix of investments will yield the highest expected return while controlling or limiting exposure to risk. Supply-network design models provide plant and distribution center locations that will minimize costs while still meeting customer service requirements. Given historical data, retail price markdown models yield revenue-maximizing discount levels and the timing of discount offers when goods have not sold as planned. All of these models are known as optimization models, that is, models that give the best decision subject to the constraints of the situation. Another type of modeling in the prescriptive analytics category is simulation optimization which combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain settings. Finally, the techniques of decision analysis can be used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. Decision analysis also employs utility theory, which assigns values to outcomes based on the decision maker’s attitude toward risk, loss, and other factors. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 6 Chapter 1 Table 1.1 Chapter 1 Introduction to Business Analytics Coverage of Business Analytics Topics in This Text Title Introduction Descriptive Predictive Prescriptive ● ● ● 2 Descriptive Statistics ● 3 Data Visualization ● 4 Data Wrangling ● 5 Probability: An Introduction to Modeling Uncertainty ● 6 Descriptive Data Mining ● ● 7 Statistical Inference 8 Linear Regression ● 9 Time Series & Forecasting ● 10 Predictive Data Mining: Regression Tasks ● 11 Predictive Data Mining: Classification Tasks ● 12 Spreadsheet Models ● ● ● 13 Monte Carlo Simulation 14 Linear Optimization Models ● 15 Integer Optimization Models ● ● ● 16 Nonlinear Optimization Models ● 17 Decision Analysis ● In this text, we cover all three areas of business analytics: descriptive, predictive, and prescriptive. Table 1.1 shows how the chapters cover the three categories. 1.4 Big Data, the Cloud, and Artificial Intelligence On any given day, Google receives over 8.5 billion searches, WhatsApp users exchange up to 65 billion messages, and Internet users generate about 2.5 quintillion bytes of data.3 It is through technology that we have truly been thrust into the data age. Because data can now be collected electronically, the available amounts of it are staggering. The Internet, cell phones, retail checkout scanners, surveillance video, and sensors on everything from aircraft to cars to bridges allow us to collect and store vast amounts of data in real time. In the midst of all of this data collection, the term big data has been created. There is no universally accepted definition of big data. However, probably the most accepted and most general definition is that big data is any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software. IBM describes the phenomenon of big data through the four Vs: volume, velocity, variety, and veracity, as shown in Figure 1.1. Volume Because data are collected electronically, we are able to collect more of it. To be useful, these data must be stored, and this storage has led to vast quantities of data. Many companies now store in excess of 100 terabytes of data (a terabyte is 1,024 gigabytes). Velocity Real-time capture and analysis of data present unique challenges both in how data are stored and the speed with which those data can be analyzed for decision making. For https://techjury.net/blog/big-data-statistics/#gref 3 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 1.4 Big Data, the Cloud, and Artificial Intelligence Figure 1.1 7 The Four Vs of Big Data Data at Rest Terabytes to exabytes of existing data to process Volume Data in Motion Streaming data, milliseconds to seconds to respond Velocity Data in Many Forms Structured, unstructured, text, multimedia Variety Data in Doubt Veracity Uncertainty due to data inconsistency and incompleteness, ambiguities, latency, deception, model approximations Source: IBM. example, the New York Stock Exchange collects 1 terabyte of data in a single trading session, and having current data and real-time rules for trades and predictive modeling are important for managing stock portfolios. Variety In addition to the sheer volume and speed with which companies now collect data, more complicated types of data are now available and are proving to be of great value to businesses. Text data are collected by monitoring what is being said about a company’s products or services on social media platforms such as Twitter. Audio data are collected from service calls (on a service call, you will often hear “this call may be monitored for quality control”). Video data collected by in-store video cameras are used to analyze shopping behavior. Analyzing information generated by these nontraditional sources is more complicated in part because of the processing required to transform the data into a numerical form that can be analyzed. Veracity Veracity has to do with how much uncertainty is in the data. For example, the data could have many missing values, which makes reliable analysis a challenge. Inconsistencies in units of measure and the lack of reliability of responses also increase the complexity of the data. Businesses have realized that understanding big data can lead to a competitive advantage. Although big data represents opportunities, it also presents challenges in terms of data storage and processing, security, and available analytical talent. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 8 Chapter 1 Introduction to Business Analytics The four Vs indicate that big data creates challenges in terms of how these complex data can be captured, stored, processed, secured, and then analyzed. Traditional databases typically assume that data fit into nice rows and columns, but that is not always the case with big data. Also, the sheer volume (the first V) often means that it is not possible to store all of the data on a single computer. This has led to technologies such as Hadoop—an opensource programming environment that supports big data processing through distributed storage and distributed processing on clusters of computers. Essentially, Hadoop provides a divide-and-conquer approach to handling massive amounts of data, dividing the storage and processing over multiple computers. MapReduce is a programming model used within Hadoop that performs the two major steps for which it is named: the map step and the reduce step. The map step divides the data into manageable subsets and distributes it to the computers in the cluster (often termed nodes) for storing and processing. The reduce step collects answers from the nodes and combines them into an answer to the original problem. Technologies such as Hadoop and MapReduce, paired with relatively inexpensive computer power, enable cost-effective processing of big data; otherwise, in some cases, processing might not even be possible. The massive amounts of available data have led to numerous innovations that help make the data useful for decision making. Cloud computing, also known simply as “the cloud,” refers to the use of data and software on servers housed external to an organization via the internet. The cloud has made storing and processing massive amounts of data feasible and cost effective. Companies pay a fee to store their big data and use software stored on servers with cloud alternatives such as Amazon Web Services (AWS), Microsoft Azure, and Oracle Cloud. Storing so much data has raised concerns about the security. Medical records, bank account information, and credit card transactions, for example, are all highly confidential and must be protected from computer hackers. Data security, the protection of stored data from destructive forces or unauthorized users, is of critical importance to companies. For example, credit card transactions are potentially very useful for understanding consumer behavior, but compromise of these data could lead to unauthorized use of the credit card or identity theft. Companies such as Target, Anthem, JPMorgan Chase, Yahoo!, Facebook, Marriott, Equifax, and Home Depot have faced major data breaches costing millions of dollars. In addition to increased interest in cloud computing and data security, big data has accelerated the development of applications of artificial intelligence. Broadly speaking, artificial intelligence (AI) is the use of big data and computers to make decisions that in the past would have required human intelligence. Often, AI software mimics the way we understand the human brain functions. AI uses many of the techniques of analytics, but often in real time, with massive amounts of data required to “train” algorithms, to complete an automated task. Applications of AI include for example, facial recognition for security checkpoints and self-driving vehicles. The complexities of the big data have increased the demand for analysts, but a shortage of qualified analysts has made hiring more challenging. More companies are searching for data scientists, who know how to effectively process and analyze massive amounts of data because they are well trained in both computer science and statistics. Next we discuss three examples of how companies are collecting big data for competitive advantage. Kroger Understands Its Customers Kroger is the largest retail grocery chain in the United States. It sends over 11 million pieces of direct mail to its customers each quarter. The quarterly mailers each contain 12 coupons that are tailored to each household based on several years of shopping data obtained through its customer loyalty card program. By collecting and analyzing consumer behavior at the individual household level, and better matching its coupon offers to shopper interests, Kroger has been able to realize a far higher redemption rate on its coupons. In the six-week period following distribution of the mailers, over 70% of households redeem at least one coupon, leading to an estimated coupon revenue of $10 billion for Kroger. (Source: Forbes.com) Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 1.5 Business Analytics in Practice 9 MagicBand at Disney The Walt Disney Company offers a wristband to visitors to its Orlando, Florida, Disney World theme park. Known as the MagicBand, the wristband contains technology that can transmit more than 40 feet and can be used to track each visitor’s location in the park in real time. The band can link to information that allows Disney to better serve its visitors. For example, prior to the trip to Disney World, a visitor might be asked to fill out a survey on their birth date and favorite rides, characters, and restaurant table type and location. This information, linked to the MagicBand, can allow Disney employees using smartphones to greet you by name as you arrive, offer you products they know you prefer, wish you a happy birthday, have your favorite characters show up as you wait in line or have lunch at your favorite table. The MagicBand can be linked to your credit card, so there is no need to carry cash or a credit card. And during your visit, your movement throughout the park can be tracked and the data can be analyzed to better serve you during your next visit to the park. (Source: Wired.com) Coca-Cola Freestyle Gives Consumers Their Own Personal Soft Drinks Coca-Cola, the largest beverage company in the world, sells its products in over 200 countries. One of CocaCola’s innovations is Coca-Cola Freestyle, a touch screen self-service soda dispenser that can be found in numerous restaurants such as Subway and Burger King. Coca-Cola Freestyle allows the customer to create their own customized drink by combining existing flavors. For example, a customer might combine Sprite with Hi-C Orange to create a flavor that is not available in stores. But Coca-Cola Freestyle is not only an innovation that better serves customers through mass customization—the machines are also a goldmine of customer preference data. The Freestyle machines collect data on mixes that consumers around the world choose. These data are analyzed to better understand customer preferences and how they differ by country and region. Whereas in the past, marketing analysts would need to rely on expensive focus groups and multiple rounds of market testing to help develop new products, Freestyle machines directly provide data on consumer preferences. For example, a new bottled product, Cherry Sprite, was launched when Freestyle data indicated that demand for that combination of flavors would be strong in the United States. (Source: buzzfeednews.com) Although big data is clearly one of the drivers for the strong demand for analytics, it is important to understand that, in some sense, big data issues are a subset of analytics. Many very valuable applications of analytics do not involve big data, but rather traditional data sets that are very manageable by traditional database and analytics software. The key to analytics is that it provides useful insights and better decision making using the data that are available—whether those data are “big” or not. 1.5 Business Analytics in Practice Business analytics involves tools as simple as reports and graphs to those that are as sophisticated as optimization, data mining, and simulation. In practice, companies that apply analytics often follow a trajectory similar to that shown in Figure 1.2. Organizations start with basic analytics in the lower left. As they realize the advantages of these analytic techniques, they often progress to more sophisticated techniques in an effort to reap the derived competitive advantage. Therefore, predictive and prescriptive analytics are sometimes referred to as advanced analytics. Not all companies reach that level of usage, but those that embrace analytics as a competitive strategy often do. Analytics has been applied in virtually all sectors of business and government. Organizations such as Procter & Gamble, IBM, UPS, Netflix, Amazon.com, Google, the Internal Revenue Service, and General Electric have embraced analytics to solve important problems or to achieve a competitive advantage. In this section, we briefly discuss some of the types of applications of analytics by application area. Accounting Analytics Applications of analytics in accounting are numerous and growing. For example, budget planning relies heavily on analytics. To construct a budget, predictive analytics in the form Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Chapter 1 Introduction to Business Analytics Figure 1.2 Competitive Advantage 10 The Spectrum of Business Analytics Optimization Decision Analysis Rule-Based Models Simulation Predictive Modeling Forecasting Data Mining Descriptive Statistics Data Visualization Data Query Standard Reporting Prescriptive Predictive Descriptive Degree of Complexity Source: Adapted from SAS. forecasts are usually required for estimates of market demand for goods and services, exchange and inflation rates, and cost projections to name a few. Furthermore, risk analysis, usually through simulation, helps ensure that the budget is robust in the face of uncertainty. Also, the use of data dashboards to monitor the financial health of companies over time is quite common. By analyzing financial records and financial transaction data, forensic accounting investigates incidents such as fraud, bribery, embezzlement, and money laundering. Descriptive and predictive analytics can be used to detect unusual patterns in transactions and to find outlier data that could be an indication of illegal behavior. Financial Analytics The financial services sector relies heavily on descriptive, predictive, and prescriptive analytics. Descriptive analytics, through the use of data visualization, is used to monitor financial performance, including stock returns, trading volumes, and measures of market return and volatility. Predictive models are used to forecast financial performance, to assess the risk of investment portfolios and projects, and to construct financial instruments such as derivatives. Prescriptive models are used to construct optimal portfolios of investments, to allocate assets, and to create optimal capital budgeting plans. Human Resource (HR) Analytics A relatively new area of application for analytics is the management of an organization’s human resources. The HR function is charged with ensuring that the organization (1) has the mix of skill sets necessary to meet its needs, (2) is hiring the highest-quality talent and providing an environment that retains it, and (3) achieves its organizational diversity goals. Google refers to its HR Analytics function as “people analytics.” Google has analyzed substantial data on their own employees to determine the characteristics of great leaders, to assess factors that contribute to productivity, and to evaluate potential new hires. Google also uses predictive analytics to continually update their forecast of future employee turnover and retention. Marketing Analytics Marketing is one of the fastest-growing areas for the application of analytics. A better understanding of consumer behavior through the use of scanner data and data generated from social media has led to an increased interest in marketing analytics. As a result, Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 1.5 Business Analytics in Practice 11 descriptive, predictive, and prescriptive analytics are all heavily used in marketing. A better understanding of consumer behavior through analytics leads to the better use of advertising budgets, more effective pricing strategies, improved forecasting of demand, improved product-line management, and increased customer satisfaction and loyalty. Predictive models and optimization are used to better align advertising to specific target audiences, making those marketing efforts much more effective and efficient. Likewise, search engine optimization increases the effectiveness of product searches, which leads to increased sales from e-commerce customers. Sentiment analysis allows companies to monitor the reactions of their customers to the goods and services they offer and adjust their service and product offerings based on that data. Through sentiment analysis, “the voice of the customer” is more powerful than ever before. Finally, the use of analytics to determine prices to charge for products and to optimally mark down the price of seasonal goods over time have helped increase profitability in the retail space. Revenue management adjusts prices for perishable goods and services based on changes in demand over time. This has led to increased profitability for airlines, hotels, rental car companies, and sports franchises. Health Care Analytics The use of analytics in health care is on the increase because of pressure to simultaneously control costs and provide more effective treatment. Descriptive, predictive, and prescriptive analytics are used to improve patient, staff, and facility scheduling; patient flow; purchasing; and inventory control. Predictive analytics is used to predict which patients are most likely to not adhere to their required medications so that proactive reminders can be sent to help these patients. Prescriptive analytics has been used directly to provide better patient outcomes. For example, researchers at Georgia Tech helped develop approaches that optimally place radiation seeds to treat prostate cancer so that the cancer is eradicated while doing minimal damage to healthy tissue. Supply Chain Analytics The core service of companies such as UPS and FedEx is the efficient delivery of goods, and analytics has long been used to achieve efficiency. The optimal sorting of goods, vehicle and staff scheduling, and vehicle routing are all key to profitability for logistics companies such as UPS and FedEx. While supply chain analytics has historically focused almost exclusively on efficiency, the supply chain problems caused by the COVID-19 pandemic and world conflicts have expanded the use of analytics to increase the resiliency of the supply chain. Monitoring and quantifying the riskiness of the supply chain and taking data-driven action to ensure the supply chain can support its mission over a robust set of scenarios has taken on new importance. Descriptive analytics is being used to more closely monitor supply chain performance, predictive analytics quantifies risk, and prescriptive analytics is used with scenario analysis to prepare supply chain solutions that can handle a high degree of disruption. Analytics for Government and Nonprofits Government agencies and other nonprofits have used analytics to drive out inefficiencies and increase the effectiveness and accountability of programs. Indeed, much of advanced analytics has its roots in the U.S. and English military dating back to World War II. Today, the use of analytics in government is becoming pervasive in everything from elections to tax collection. The U.S. Internal Revenue Service has used data mining to identify patterns that distinguish questionable annual personal income tax filings. In one application, the IRS compares its data on individual taxpayers with data received from banks on mortgage payments made by those taxpayers. When taxpayers report a mortgage payment that is unrealistically high relative to their reported taxable income, they are flagged as possible underreporters of taxable income. The filing is then further scrutinized and may trigger an audit. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 12 Chapter 1 Introduction to Business Analytics Likewise, nonprofit agencies have used analytics to ensure their effectiveness and accountability to their donors and clients. Descriptive and predictive analytics are used to monitor agency performance as well as track donor behavior and forecast donations. Fundraising analytics, using techniques from data mining, are used to identify potential donors and minimize donor attrition. Optimization is used to allocate scarce resources in the capital budgeting process. Sports Analytics The use of analytics in sports has gained considerable notoriety since 2003 when renowned author Michael Lewis published Moneyball. Lewis’ book tells the story of how the Oakland Athletics used an analytical approach to player evaluation in order to assemble a competitive team with a limited budget. The use of analytics for player evaluation and on-field strategy is now common, especially in professional sports. Professional sports teams use analytics to assess players for the amateur drafts and to decide how much to offer players in contract negotiations; and teams use analytics to assist with on-field decisions such as which pitchers to use in various games of a Major League Baseball playoff series. The use of analytics for off-the-field business decisions is also increasing rapidly. Ensuring customer satisfaction is important for any company, and fans are the customers of sports teams. Based on fan survey data, the Cleveland Guardians professional baseball team used a type of predictive modeling known as conjoint analysis to design its premium seating offerings at Progressive Field. Using prescriptive analytics, franchises across several major sports dynamically adjust ticket prices throughout the season to reflect the relative attractiveness and potential demand for each game. Web Analytics Web analytics is the analysis of online activity, which includes, but is not limited to, visits to web sites and social media sites such as Facebook and LinkedIn. Web analytics has huge implications for promoting and selling products and services via the Internet. Leading companies apply descriptive and advanced analytics to data collected in online experiments to determine the best way to configure web sites, position ads, and utilize social networks for the promotion of products and services. Online experimentation involves exposing various subgroups to different versions of a web site and tracking the results. Because of the massive pool of Internet users, experiments can be conducted without risking the disruption of the overall business of the company. Such experiments are proving to be invaluable because they enable the company to use trial-and-error in determining statistically what makes a difference in their web site traffic and sales. 1.6 Legal and Ethical Issues in the Use of Data and Analytics With the advent of big data and the dramatic increase in the use of analytics and data science to improve decision making, increased attention has been paid to ethical concerns around data privacy and the ethical use of models based on data. As businesses routinely collect data about their customers, they have an obligation to protect the data and to not misuse that data. Clients and customers have an obligation to understand the trade-offs between allowing their data to be collected and used, and the benefits they accrue from allowing a company to collect and use that data. For example, many companies have loyalty cards that collect data on customer purchases. In return for the benefits of using a loyalty card, typically discounted prices, customers must agree to allow the company to collect and use the data on purchases. An agreement must be signed between the customer and the company, and the agreement must specify what data will be collected and how it will be used. For example, the agreement might say that all scanned purchases will be collected with the date, time, location, and card number, but that the company agrees to only use that data internally to the company and to not give or sell that data to outside firms or individuals. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 1.6 Legal and Ethical Issues in the Use of Data and Analytics 13 The company then has an ethical obligation to uphold that agreement and make every effort to ensure that the data are protected from any type of unauthorized access. Unauthorized access of data is known as a data breach. Data breaches are a major concern for all companies in the digital age. A study by IBM and the Ponemon Institute estimated that the average cost of a data breach is $3.86 million. Data privacy laws are designed to protect individuals’ data from being used against their wishes. One of the strictest data privacy laws is the General Data Protection Regulation (GDPR) which went into effect in the European Union in May 2018. The law stipulates that the request for consent to use an individual’s data must be easily understood and accessible, the intended uses of the data must be specified, and it must be easy to withdraw consent. The law also stipulates that an individual has a right to a copy of their data and the right “to be forgotten,” that is, the right to demand that their data be erased. It is the responsibility of analytics professionals, indeed, anyone who handles or stores data, to understand the laws associated with the collection, storage, and use of individuals’ data. Ethical issues that arise in the use of data and analytics are just as important as the legal issues. Analytics professionals have a responsibility to behave ethically, which includes protecting data, being transparent about the data and how it was collected, and what it does and does not contain. Analysts must be transparent about the methods used to analyze the data and any assumptions that have to be made for the methods used. Finally, analysts must provide valid conclusions and understandable recommendations to their clients. Intentionally using biased data to achieve a goal is inherently unethical. Misleading a client by misrepresenting results is clearly unethical. For example, consider the case of an airline that runs an advertisement that “it is preferred by 84% of business fliers to Chicago.” Such a statement is valid if the airline randomly surveyed business fliers across all airlines with a destination of Chicago. But, if for convenience, the airline surveyed only its own customers, the survey would be biased, and the claim would be misleading because fliers on other airlines were not surveyed. Indeed, if anything, the only conclusion one can legitimately draw from the biased sample of its own customers would be that 84% of that airlines’ own customers preferred that airline and 16% of its own customers actually preferred another airline! In the book, Weapons of Math Destruction, author Cathy O’Neil discusses how algorithms and models can be unintentionally biased. For example, consider an analyst who is building a credit risk model for awarding loans. The location of the home of the applicant might be a variable that is correlated with other variables such as income and ethnicity. Income is perhaps a relevant variable for determining the amount of a loan, but ethnicity is not. A model using home location could therefore lead to unintentional bias in the credit risk model. It is the analysts’ responsibility to make sure this type of model bias and data bias do not become a part of the model. Researcher and opinion writer Zeynep Tufecki examines so-called “unintended consequences” of analytics and artificial intelligence, and particularly of machine learning and recommendation engines. Many Internet sites that use recommendation engines often suggest more extreme content, in terms of political views and conspiracy theories, to users based on their past viewing history. Tufecki and others theorize that this is because the machine learning algorithms being used have identified that more extreme content increases users’ viewing time on the site, which is often the objective function being maximized by the machine learning algorithm. Therefore, while it is not the intention of the algorithm to promote more extreme views and disseminate false information, this may be the unintended consequence of using a machine learning algorithm that maximizes users’ viewing time on the site. Analysts and decision makers must be aware of potential unintended consequences of their models, and they must decide how to react to these consequences once they are discovered. Several organizations, including the American Statistical Association (ASA) and the Institute for Operations Research and the Management Sciences (INFORMS), provide ethical guidelines for analysts. In their “Ethical Guidelines for Statistical Practice,” the ASA uses the term statistician throughout, but states that this “includes all practitioners Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 14 Chapter 1 Introduction to Business Analytics of statistics and quantitative sciences—regardless of job title or field of degree— comprising statisticians at all levels of the profession and members of other professions who utilize and report statistical analyses and their applications.” Their guidelines state that “Good statistical practice is fundamentally based on transparent assumptions, reproducible results, and valid interpretations.” The report contains 52 guidelines organized into 8 topic areas. We encourage you to read and familiarize yourself with these guidelines. INFORMS is a professional society focused on operations research and the management sciences, including analytics. INFORMS offers an analytics certification called CAP— certified analytics professional. All candidates for CAP are required to comply with the code of ethics/conduct provided by INFORMS. The INFORMS CAP guidelines state, “In general, analytics professionals are obliged to conduct their professional activities responsibly, with particular attention to the values of consistency, respect for individuals, autonomy of all, integrity, justice, utility and competence.” INFORMS also offers a set of Ethics Guidelines for its members, which covers ethical behavior for analytics professionals in three domains: Society, Organizations (businesses, government, nonprofit organization, and universities), and the Profession (operations research and analytics). As these guidelines are fairly easy to understand and at the same time fairly comprehensive, we list them here in Table 1.2 and encourage you as a user/provider of analytics to make them your guiding principles. Table 1.2 INFORMS Ethics Guidelines Relative to Society Analytics professionals should aspire to be: Accountable for their professional actions and the impact of their work. ● Forthcoming about their assumptions, interests, sponsors, motivations, limitations, and potential conflicts of interest. ● Honest in reporting their results, even when they fail to yield the desired outcome. ● Objective in their assessments of facts, irrespective of their opinions or beliefs. ● Respectful of the viewpoints and the values of others. ● Responsible for undertaking research and projects that provide positive benefits by advancing our scientific understanding, contributing to organizational improvements, and supporting social good. ● Relative to Organizations Analytics professionals should aspire to be: Accurate in our assertions, reports, and presentations. ● Alert to possible unintended or negative consequences that our results and recommendations may have on others. ● Informed of advances and developments in the fields relevant to our work. ● Questioning of whether there are more effective and efficient ways to reach a goal. ● Realistic in our claims of achievable results, and in acknowledging when the best course of action may be to terminate a project. ● Rigorous by adhering to proper professional practices in the development and reporting of our work. ● Relative to the Profession Analytics professionals should aspire to be: Cooperative by sharing best practices, information, and ideas with colleagues, young professionals, and students. ● Impartial in our praise or criticism of others and their accomplishments, setting aside personal interests. ● Inclusive of all colleagues, and rejecting discrimination and harassment in any form. ● Tolerant of well-conducted research and well-reasoned results, which may differ from our own findings or opinions. ● Truthful in providing attribution when our work draws from the ideas of others. ● Vigilant by speaking out against actions that are damaging to the profession ● Source: INFORMS Ethics Guidelines. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Glossary 15 Summary This introductory chapter began with a discussion of decision making. Decision making can be defined as the following process: (1) identify and define the problem, (2) determine the criteria that will be used to evaluate alternative solutions, (3) determine the set of alternative solutions, (4) evaluate the alternatives, and (5) choose an alternative. Decisions may be strategic (high level, concerned with the overall direction of the business), tactical (midlevel, concerned with how to achieve the strategic goals of the business), or operational (day-to-day decisions that must be made to run the company). Uncertainty and an overwhelming number of alternatives are two key factors that make decision making difficult. Business analytics approaches can assist by identifying and mitigating uncertainty and by prescribing the best course of action from a very large number of alternatives. In short, business analytics can help us make better-informed decisions. There are three categories of analytics: descriptive, predictive, and prescriptive. Descriptive analytics describes what has happened and includes tools such as reports, data visualization, data dashboards, descriptive statistics, and unsupervised learning techniques from data mining. Predictive analytics consists of techniques that use past data to predict future events or ascertain the impact of one variable on another. These techniques include regression, supervised learning techniques from data mining, forecasting, and simulation. Prescriptive analytics uses data to determine a course of action. This class of analytical techniques includes rule-based models, simulation, decision analysis, and optimization. Descriptive and predictive analytics can help us better understand the uncertainty and risk associated with our decision alternatives. Predictive and prescriptive analytics, also often referred to as advanced analytics, can help us make the best decision when facing a myriad of alternatives. Big data is a set of data that is too large or too complex to be handled by standard data-processing techniques or typical desktop software. Cloud computing has made storing and processing vast amounts of data more efficient and more cost effective. The increasing prevalence of big data is leading to an increase in the use of analytics and artificial intelligence. The Internet, retail scanners, and cell phones are making huge amounts of data available to companies, and these companies want to better understand these data. Business analytics helps them understand these data and use them to make better decisions. We also discussed various application areas of analytics. Our discussion focused on accounting analytics, financial analytics, human resource analytics, marketing analytics, health care analytics, supply chain analytics, analytics for government and nonprofit organizations, sports analytics, and web analytics. However, the use of analytics is rapidly spreading to other sectors, industries, and functional areas of organizations. We concluded this chapter with a discussion of legal and ethical issues in the use of data and analytics, a topic that should be of great importance to all practitioners and consumers of analytics. Each remaining chapter in this text will provide a real-world vignette in which business analytics is applied to a problem faced by a real organization. Glossary Artificial intelligence (AI) The use of data and computers to make decisions that would have in the past required human intelligence. Advanced analytics Predictive and prescriptive analytics. Big data Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software. Business analytics The scientific process of transforming data into insight for making better decisions. Cloud computing The use of data and software on severs housed external to an organization via the internet. Also known simply as “the cloud.” Data dashboard A collection of tables, charts, and maps to help management monitor selected aspects of the company’s performance. Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 16 Chapter 1 Introduction to Business Analytics Data mining The use of analytical techniques for better understanding patterns and relationships that exist in large data sets. Data query A request for information with certain characteristics from a database. Data scientists Analysts trained in both computer science and statistics who know how to effectively process and analyze massive amounts of data. Data security Protecting stored data from destructive forces or unauthorized users. Decision analysis A technique used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. Descriptive analytics Analytical tools that describe what has happened. Hadoop An open-source programming environment that supports big data processing through distributed storage and distributed processing on clusters of computers. MapReduce Programming model used within Hadoop that performs the two major steps for which it is named: the map step and the reduce step. Operational decision A decision concerned with how the organization is run from day to day. Optimization model A mathematical model that gives the best decision, subject to the situation’s constraints. Predictive analytics Techniques that use models constructed from past data to predict the future or to ascertain the impact of one variable on another. Prescriptive analytics Techniques that analyze input data and yield a best course of action. Rule-based model A prescriptive model that is based on a rule or set of rules. Simulation The use of probability and statistics to construct a computer model to study the impact of uncertainty on the decision at hand. Simulation optimization The use of probability and statistics to model uncertainty, combined with optimization techniques, to find good decisions in highly complex and highly uncertain settings. Strategic decision A decision that involves higher-level issues and that is concerned with the overall direction of the organization, defining the overall goals and aspirations for the organization’s future. Supervised learning A category of data-mining techniques in which an algorithm learns how to predict an outcome variable of interest. Tactical decision A decision concerned with how the organization should achieve the goals and objectives set by its strategy. Unsupervised learning A category of data-mining techniques in which an algorithm explains relationships without an outcome variable to guide the process. Utility theory The study of the total worth or relative desirability of a particular outcome that reflects the decision maker’s attitude toward a collection of factors such as profit, loss, and risk. Problems 1. Quality Versus Low Cost. Car manufacturer Lexus competes in the luxury car category and is known for its quality. The decision to compete on quality rather than low cost is an example of what type of decision: strategic, tactical, or operational? Explain. LO 1 2. Package Delivery. Every day, logistics companies such as United Parcel Service (UPS) must decide how to route their trucks, that is, the order in which to deliver the packages that have been loaded on a truck. UPS developed a system called ORION (On-Road Integrated Optimization and Navigation) which dynamically routes its trucks based on packages on the truck and traffic conditions. It is estimated that ORION saves UPS about 10 million gallons of fuel per year. Is the decision of how to route a truck a strategic, tactical, or operational decision? Explain. LO 1 3. Airline Decisions. JetBlue is a major U.S. airline that ranked seventh in the U.S. in 2021 based on number of passengers carried. JetBlue has 270 aircraft and services Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 17 Problems 104 destinations. Consider three decisions below that an airline such as JetBlue must make. For each decision, tell if it is strategic, tactical, or operational and explain. LO 1 a. Which crew should be assigned to a given flight? b. Should we compete on low cost or quality? c. What type of aircraft should we purchase? 4. Choosing a College. Millions of high school graduates decide annually to enroll in a college or university. Many factors can influence a student’s choice of where to enroll. List the five steps of the decision-making process and discuss each step of the process, where the decision to be made is choosing which college to attend. LO 2 5. Package Delivery (Revisited). Consider again the ORION system discussed in problem 2. Is ORION an example of descriptive, predictive, or prescriptive analytics? Explain. LO 3 6. Quality Control for Boxes of Cereal. A control chart is a graphical tool to help determine if a process may be exhibiting non-random variation that needs to be investigated. If non-random variation is present, the process is said to be “out of control” otherwise it is said to be “in control.” The following figure shows a control chart for a production line that fills boxes of cereal. Based on past data, we can calculate the mean weight of a box of cereal when the process is in control. The mean weight is 16.05 ounces. We can also calculate control limits, an upper control limit (UCL) and a lower control limit (LCL). New samples are collected over time and the data indicates that the process is in control so long as the new sample weights are between UCL and LCL. As shown in the chart, only sample 5 is outside of the control limits. LO 3 16.20 UCL 5 16.17 Sample Mean x 16.15 16.10 16.05 Process Mean 16.00 15.95 LCL 5 15.93 15.90 Process out of control 1 2 3 4 5 6 7 8 9 10 Sample Number a. Is the control chart an example of descriptive, predictive, or prescriptive analytics? b. Suppose the control cart is part of a data dashboard and the chart is combined with a rule that does the following. If four consecutive sample mean weights are outside of the control limits, the production line is automatically stopped, and a message appears on the dashboard. The message says “The production line is stopped. The process may be out of control. Please inspect the fill machine.” Is this new enhanced control chart combined with a rule an example of descriptive, predictive, or prescriptive analytics? 7. Amazon Books. An example of a response from Amazon when this textbook you are reading was chosen online follows. It indicates that some people who purchased this text also tended to purchase The World Is Flat, Fundamentals of Corporate Finance, and Strategic Marketing Management. In this application of analytics, is Amazon using descriptive, predictive, or prescriptive analytics? Explain. LO 3 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 18 Chapter 1 Introduction to Business Analytics 8. Employee Retention. Human resource (HR) analytics or people analytics are terms used for the use of analytics to manage a company’s workforce. Google, Microsoft, and Walmart, for example, use people analytics to help retain their best people, ensure a diverse workforce, and better understand the strengths and areas needing improvement. LO 3, 4 a. One application of people analytics is to build a model that estimates the probability of an employee departing the company within the next six months. Inputs to the model could be market demand for the skills the person possesses, how long the person has been with the company, and a major life event recently occurring for the person (e.g., divorce). Is this type of model descriptive, predictive, or prescriptive? Explain. b. How could you use the model described in part (a) to help improve the workforce? 9. Supermarket Checkout Lanes. A supermarket has been experiencing long lines during peak periods of the day. The problem is noticeably worse on certain days of the week, and the peak periods are sometimes different according to the day of the week. There are usually enough workers on the job to open all checkout lanes. The difficulty for store managers is knowing when to call some of the workers stocking shelves up to the front of the store to work the checkout lanes. LO 4 a. How could analytics be used to help the supermarket? b. What data would be needed? 10. Pricing Subcompact Rental Cars. Setting the right price for a product is an important business decision. If the product is priced too high, the demand could be very low. Set the price too low, demand may be high, but we are potentially leaving money on the table because the revenue per unit is low. Pricing analytics involves finding the right tradeoff between price charged and demand so as to maximize revenue. Suppose we need to set the price for renting a subcompact automobile for one day. Let us outline the decision process: Step 1. Identify and define the problem. We need to set a price per day for a midsize rental car. Step 2. Determine the criteria that will be used to evaluate alternative solutions. Our goal in setting the price is to maximize revenue per day. Step 3. Determine the set of alternative solutions. Based on historical data and the competition, we will consider a broad price range from $10 per day to $60 per day per car. Step 4. Evaluate the alternatives. We will evaluate proposed prices based on the expected revenue per day. Step 5. Choose an alternative. We will choose the price that maximizes expected revenue. We can use data and analytics to complete steps 4 and 5 of the decision process. LO 3, 4 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com 19 Problems a. Based on historical or test-market data, we can estimate a model that gives expected revenue as a function of price, as shown below. The dots represent the data (price and demand combinations) and the estimated model is the line in the chart: Demand 5 21.4028(Price) 1 102.65. For example, for price of $35, Demand 5 21.4028(35) 1 102.65 5 53.552 vehicles. So, we estimate that at a price of $35 per day, the demand will be about 54 vehicles. Is this estimated equation, a descriptive, predictive, or prescriptive model? Explain. Demand (number of vehicles) 90 80 70 60 50 40 30 20 Demand 5 21.4028(Price) 1 102.65 10 0 0 10 20 30 40 Price ($) 50 60 70 Our goal (step 5) is to find the price that maximizes expected revenue. Revenue 5 demand 3 price which is (21.4028(Price) 1 102.65) 3 (Price) 5 21.40228(Price)2 1 102.65(Price). The revenue as a function of price is shown below for $10 increments of price. b. What is the price that maximizes revenue? c. Is step 5, visually inspecting the revenue function to find a revenue-maximizing price, descriptive, predictive, or prescriptive analytics? Explain. Revenue ($) 2,000 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 0 10 20 30 40 50 Price ($) 60 70 80 Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Get Complete eBook Download By email at student.support@hotmail.com Get Complete eBook Download By email at student.support@hotmail.com Copyright 2024 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.